Abstract In this study, a novel surrogate model assisted multiobjective memetic algorithm (SMOMA) is developed for optimal pumping strategies of large-scale coastal groundwater problems. The proposed SMOMA integrates an efficient data-driven surrogate model with an improved non-dominated sorted genetic algorithm-II (NSGAII) that employs a local search operator to accelerate its convergence in optimization. The surrogate model based on Kernel Extreme Learning Machine (KELM) is developed and evaluated as an approximate simulator to generate the patterns of regional groundwater flow and salinity levels in coastal aquifers for reducing huge computational burden. The KELM model is adaptively trained during evolutionary search to satisfy desired fidelity level of surrogate so that it inhibits error accumulation of forecasting and results in correctly converging to true Pareto-optimal front. The proposed methodology is then applied to a large-scale coastal aquifer management in Baldwin County, Alabama. Objectives of minimizing the saltwater mass increase and maximizing the total pumping rate in the coastal aquifers are considered. The optimal solutions achieved by the proposed adaptive surrogate model are compared against those solutions obtained from one-shot surrogate model and original simulation model. The adaptive surrogate model does not only improve the prediction accuracy of Pareto-optimal solutions compared with those by the one-shot surrogate model, but also maintains the equivalent quality of Pareto-optimal solutions compared with those by NSGAII coupled with original simulation model, while retaining the advantage of surrogate models in reducing computational burden up to 94% of time-saving. This study shows that the proposed methodology is a computationally efficient and promising tool for multiobjective optimizations of coastal aquifer managements.

中文翻译：

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基于自适应替代模型的沿海含水层管理多目标优化

摘要 在这项研究中，一种新的代理模型辅助多目标模因算法 (SMOMA) 被开发用于大规模沿海地下水问题的最佳抽水策略。所提出的 SMOMA 将有效的数据驱动代理模型与改进的非支配排序遗传算法 II (NSGAII) 相结合，该算法采用局部搜索算子来加速其优化收敛。基于内核极限学习机（KELM）的替代模型被开发和评估为近似模拟器，以生成沿海含水层中区域地下水流和盐度水平的模式，以减少巨大的计算负担。KELM 模型在进化搜索过程中经过自适应训练，以满足期望的代理保真度水平，从而抑制预测的错误积累并导致正确收敛到真正的帕累托最优前沿。然后将提议的方法应用于阿拉巴马州鲍德温县的大规模沿海含水层管理。考虑了最大限度地减少海水质量增加和最大限度地提高沿海含水层总抽水率的目标。将提出的自适应代理模型获得的最佳解决方案与从一次性代理模型和原始仿真模型获得的解决方案进行比较。与一次性代理模型相比，自适应代理模型不仅提高了帕累托最优解的预测精度，但也保持了与 NSGAII 加上原始仿真模型的帕累托最优解相当的质量，同时保留了代理模型的优势，减少了高达 94% 的时间节省的计算负担。这项研究表明，所提出的方法是一种计算效率高且有前途的工具，可用于沿海含水层管理的多目标优化。